data_by_decade<- function(dataset){
dataset$decade <- cut(dataset$year, seq(1950,2010,10),labels = as.character(seq(1950,2000,10)))
return(dataset)
}
fun_continents <- function(continent, decade) {
a <- gapminder_unfiltered[which(gapminder_unfiltered$continent == continent & gapminder_unfiltered$decade == decade),]
stats <- c(min = min(a$lifeExp), max =max( a$lifeExp),
mean = mean(a$lifeExp), median = median(a$lifeExp),
IQR = quantile(a$lifeExp, 0.75) - quantile(a$lifeExp, 0.25))
return(stats)
}
fun_continents( "Africa", 1950)
## Warning: Unknown or uninitialised column: 'decade'.
## Warning in min(a$lifeExp): no non-missing arguments to min; returning Inf
## Warning in max(a$lifeExp): no non-missing arguments to max; returning -Inf
## min max mean median IQR.75%
## Inf -Inf NaN NA NA
gapminder_unfiltered <- data_by_decade(gapminder_unfiltered)
continentdata <- c()
sta <- c()
for(continent in levels(gapminder_unfiltered$continent)) {
for(decade in levels(gapminder_unfiltered$decade)) {
continentdata <- rbind(continentdata, c(continent, decade))
sta <- rbind(sta, fun_continents(continent, decade ))
}
}
## Warning in min(a$lifeExp): no non-missing arguments to min; returning Inf
## Warning in min(a$lifeExp): no non-missing arguments to max; returning -Inf
continentdata
## [,1] [,2]
## [1,] "Africa" "1950"
## [2,] "Africa" "1960"
## [3,] "Africa" "1970"
## [4,] "Africa" "1980"
## [5,] "Africa" "1990"
## [6,] "Africa" "2000"
## [7,] "Americas" "1950"
## [8,] "Americas" "1960"
## [9,] "Americas" "1970"
## [10,] "Americas" "1980"
## [11,] "Americas" "1990"
## [12,] "Americas" "2000"
## [13,] "Asia" "1950"
## [14,] "Asia" "1960"
## [15,] "Asia" "1970"
## [16,] "Asia" "1980"
## [17,] "Asia" "1990"
## [18,] "Asia" "2000"
## [19,] "Europe" "1950"
## [20,] "Europe" "1960"
## [21,] "Europe" "1970"
## [22,] "Europe" "1980"
## [23,] "Europe" "1990"
## [24,] "Europe" "2000"
## [25,] "FSU" "1950"
## [26,] "FSU" "1960"
## [27,] "FSU" "1970"
## [28,] "FSU" "1980"
## [29,] "FSU" "1990"
## [30,] "FSU" "2000"
## [31,] "Oceania" "1950"
## [32,] "Oceania" "1960"
## [33,] "Oceania" "1970"
## [34,] "Oceania" "1980"
## [35,] "Oceania" "1990"
## [36,] "Oceania" "2000"
sta
## min max mean median IQR.75%
## [1,] 30.000 58.089 40.38072 40.1790 6.68475
## [2,] 32.767 61.557 44.51384 44.0780 8.11725
## [3,] 35.400 67.064 48.70841 48.5635 9.44625
## [4,] 38.445 71.913 52.66606 51.6395 12.22850
## [5,] 23.599 74.772 53.75709 52.6440 12.69200
## [6,] 39.193 76.442 54.38402 52.7115 12.60675
## [7,] 37.579 71.040 58.28277 59.3110 18.66825
## [8,] 43.428 72.650 62.39196 64.9255 13.39925
## [9,] 46.714 75.140 66.09017 67.5225 10.49500
## [10,] 51.461 77.510 69.31884 70.6695 8.96975
## [11,] 55.089 79.420 71.74211 72.4920 7.37325
## [12,] 58.137 80.653 73.60153 74.2390 6.47400
## [13,] 28.801 67.840 50.37611 48.4630 18.71600
## [14,] 31.997 72.070 56.03758 56.2270 18.03950
## [15,] 31.220 76.210 61.28064 62.8954 14.12175
## [16,] 39.854 79.040 65.95619 67.1230 13.16800
## [17,] 41.674 81.350 68.69615 70.1215 12.05050
## [18,] 42.129 82.670 70.95151 72.1990 11.57725
## [19,] 43.585 74.090 67.80427 68.3700 5.07000
## [20,] 52.098 74.700 70.41874 70.4350 2.86250
## [21,] 57.005 76.710 72.18054 72.1300 3.50700
## [22,] 61.036 78.150 73.94292 74.5800 4.72400
## [23,] 66.146 79.990 75.49946 76.4800 4.81000
## [24,] 67.922 81.757 77.47489 78.4600 4.51250
## [25,] Inf -Inf NaN NA NA
## [26,] 66.654 71.200 70.10200 70.4600 0.78000
## [27,] 68.158 72.710 69.92763 69.5420 1.07250
## [28,] 69.130 71.370 70.18112 70.0800 1.48500
## [29,] 62.959 72.140 68.30447 68.5700 2.75000
## [30,] 64.878 72.962 68.88362 68.6500 4.16475
## [31,] 68.720 71.260 70.18650 70.2950 0.94250
## [32,] 49.690 71.550 67.01100 70.9800 5.79500
## [33,] 42.522 74.600 66.90520 71.7800 10.84150
## [34,] 52.460 77.060 69.53376 73.7900 9.53325
## [35,] 55.186 79.990 72.36039 76.2650 11.20900
## [36,] 56.651 81.235 72.50648 73.0100 10.79000
s <- data.frame(continentdata, sta )
names(s)[1] <- "continent"
names(s)[2] <- "decade"
s
## continent decade min max mean median IQR.75.
## 1 Africa 1950 30.000 58.089 40.38072 40.1790 6.68475
## 2 Africa 1960 32.767 61.557 44.51384 44.0780 8.11725
## 3 Africa 1970 35.400 67.064 48.70841 48.5635 9.44625
## 4 Africa 1980 38.445 71.913 52.66606 51.6395 12.22850
## 5 Africa 1990 23.599 74.772 53.75709 52.6440 12.69200
## 6 Africa 2000 39.193 76.442 54.38402 52.7115 12.60675
## 7 Americas 1950 37.579 71.040 58.28277 59.3110 18.66825
## 8 Americas 1960 43.428 72.650 62.39196 64.9255 13.39925
## 9 Americas 1970 46.714 75.140 66.09017 67.5225 10.49500
## 10 Americas 1980 51.461 77.510 69.31884 70.6695 8.96975
## 11 Americas 1990 55.089 79.420 71.74211 72.4920 7.37325
## 12 Americas 2000 58.137 80.653 73.60153 74.2390 6.47400
## 13 Asia 1950 28.801 67.840 50.37611 48.4630 18.71600
## 14 Asia 1960 31.997 72.070 56.03758 56.2270 18.03950
## 15 Asia 1970 31.220 76.210 61.28064 62.8954 14.12175
## 16 Asia 1980 39.854 79.040 65.95619 67.1230 13.16800
## 17 Asia 1990 41.674 81.350 68.69615 70.1215 12.05050
## 18 Asia 2000 42.129 82.670 70.95151 72.1990 11.57725
## 19 Europe 1950 43.585 74.090 67.80427 68.3700 5.07000
## 20 Europe 1960 52.098 74.700 70.41874 70.4350 2.86250
## 21 Europe 1970 57.005 76.710 72.18054 72.1300 3.50700
## 22 Europe 1980 61.036 78.150 73.94292 74.5800 4.72400
## 23 Europe 1990 66.146 79.990 75.49946 76.4800 4.81000
## 24 Europe 2000 67.922 81.757 77.47489 78.4600 4.51250
## 25 FSU 1950 Inf -Inf NaN NA NA
## 26 FSU 1960 66.654 71.200 70.10200 70.4600 0.78000
## 27 FSU 1970 68.158 72.710 69.92763 69.5420 1.07250
## 28 FSU 1980 69.130 71.370 70.18112 70.0800 1.48500
## 29 FSU 1990 62.959 72.140 68.30447 68.5700 2.75000
## 30 FSU 2000 64.878 72.962 68.88362 68.6500 4.16475
## 31 Oceania 1950 68.720 71.260 70.18650 70.2950 0.94250
## 32 Oceania 1960 49.690 71.550 67.01100 70.9800 5.79500
## 33 Oceania 1970 42.522 74.600 66.90520 71.7800 10.84150
## 34 Oceania 1980 52.460 77.060 69.53376 73.7900 9.53325
## 35 Oceania 1990 55.186 79.990 72.36039 76.2650 11.20900
## 36 Oceania 2000 56.651 81.235 72.50648 73.0100 10.79000
From the table, we can see the life expectancy is increasing by decades for each continent. But the IQR for developing countries, like in Asia increase first and then decrease a littel bit. And for Europe and Americas, the IQR doesn’t change a lot, it’s steady.
Now, we can consider plots to look into this further.
Europe <- subset(gapminder_unfiltered, continent == "Europe")
Africa <- subset(gapminder_unfiltered, continent == "Africa")
Asia <- subset(gapminder_unfiltered, continent == "Asia")
Americas <- subset(gapminder_unfiltered, continent == "Americas")
Oceania <- subset(gapminder_unfiltered, continent == "Oceania")
aggregate(lifeExp~continent, gapminder_unfiltered, min)
## continent lifeExp
## 1 Africa 23.599
## 2 Americas 37.579
## 3 Asia 28.801
## 4 Europe 43.585
## 5 FSU 57.300
## 6 Oceania 42.522
aggregate(lifeExp~continent, gapminder_unfiltered, max)
## continent lifeExp
## 1 Africa 76.442
## 2 Americas 80.653
## 3 Asia 82.670
## 4 Europe 81.757
## 5 FSU 72.962
## 6 Oceania 81.235
aggregate(lifeExp~continent, gapminder_unfiltered, mean)
## continent lifeExp
## 1 Africa 49.03680
## 2 Americas 67.09195
## 3 Asia 62.41587
## 4 Europe 72.72164
## 5 FSU 68.84430
## 6 Oceania 69.74691
aggregate(lifeExp~continent, gapminder_unfiltered, median)
## continent lifeExp
## 1 Africa 47.9240
## 2 Americas 69.4855
## 3 Asia 64.3330
## 4 Europe 72.7650
## 5 FSU 69.1100
## 6 Oceania 70.9900
aggregate(lifeExp~continent, gapminder_unfiltered, IQR)
## continent lifeExp
## 1 Africa 12.18600
## 2 Americas 10.92950
## 3 Asia 16.19825
## 4 Europe 5.76250
## 5 FSU 2.71050
## 6 Oceania 7.69600
filter(gapminder_unfiltered,(continent=="Europe" & year<1970 & year >= 1960)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Europe" & year<1980 & year >= 1970)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Europe" & year<1990 & year >= 1980)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Europe" & year<2000 & year >= 1990)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Europe" & year<2010 & year >= 2000)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Africa" & year<1970 & year >= 1960)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Africa" & year<1980 & year >= 1970)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Africa" & year<1990 & year >= 1980)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Africa" & year<2000 & year >= 1990)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Africa" & year<2010 & year >= 2000)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Asia" & year<1970 & year >= 1960)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Asia" & year<1980 & year >= 1970)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Asia" & year<1990 & year >= 1980)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Asia" & year<2000 & year >= 1990)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Asia" & year<2010 & year >= 2000)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Americas" & year<1970 & year >= 1960)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Americas" & year<1980 & year >= 1970)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Americas" & year<1990 & year >= 1980)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Americas" & year<2000 & year >= 1990)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Americas" & year<2010 & year >= 2000)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Oceania" & year<1970 & year >= 1960)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Oceania" & year<1980 & year >= 1970)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Oceania" & year<1990 & year >= 1980)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Oceania" & year<2000 & year >= 1990)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
filter(gapminder_unfiltered,(continent=="Oceania" & year<2010 & year >= 2000)) %>%
ggplot() +
geom_freqpoly(aes(x=lifeExp),binwidth=1)
europe <- subset(gapminder_unfiltered, continent == "Europe")
africa <- subset(gapminder_unfiltered, continent == "Africa")
asia <- subset(gapminder_unfiltered, continent == "Asia")
americas <- subset(gapminder_unfiltered, continent == "Americas")
oceania <- subset(gapminder_unfiltered, continent == "Oceania")
aggregate(gdpPercap~continent, gapminder_unfiltered, min)
## continent gdpPercap
## 1 Africa 241.1659
## 2 Americas 1201.6372
## 3 Asia 331.0000
## 4 Europe 973.5332
## 5 FSU 1442.9378
## 6 Oceania 864.9743
aggregate(gdpPercap~continent, gapminder_unfiltered, max)
## continent gdpPercap
## 1 Africa 21951.21
## 2 Americas 42951.65
## 3 Asia 113523.13
## 4 Europe 70014.00
## 5 FSU 16666.51
## 6 Oceania 36383.17
aggregate(gdpPercap~continent, gapminder_unfiltered, mean)
## continent gdpPercap
## 1 Africa 2175.859
## 2 Americas 10802.574
## 3 Asia 10073.938
## 4 Europe 16551.178
## 5 FSU 7326.686
## 6 Oceania 14057.097
aggregate(gdpPercap~continent, gapminder_unfiltered, median)
## continent gdpPercap
## 1 Africa 1190.844
## 2 Americas 6924.750
## 3 Asia 3273.138
## 4 Europe 14433.025
## 5 FSU 7050.027
## 6 Oceania 14526.125
aggregate(gdpPercap~continent, gapminder_unfiltered, IQR)
## continent gdpPercap
## 1 Africa 1614.533
## 2 Americas 10807.514
## 3 Asia 11784.104
## 4 Europe 13647.275
## 5 FSU 4507.156
## 6 Oceania 15544.632
filter(gapminder_unfiltered,(continent=="Europe" & year<1970 & year >= 1960)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Europe" & year<1980 & year >= 1970)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Europe" & year<1990 & year >= 1980)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Europe" & year<2000 & year >= 1990)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Europe" & year<2010 & year >= 2000)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Africa" & year<1970 & year >= 1960)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Africa" & year<1980 & year >= 1970)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Africa" & year<1990 & year >= 1980)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Africa" & year<2000 & year >= 1990)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Africa" & year<2010 & year >= 2000)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Asia" & year<1970 & year >= 1960)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Asia" & year<1980 & year >= 1970)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Asia" & year<1990 & year >= 1980)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Asia" & year<2000 & year >= 1990)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Asia" & year<2010 & year >= 2000)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Americas" & year<1970 & year >= 1960)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Americas" & year<1980 & year >= 1970)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Americas" & year<1990 & year >= 1980)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Americas" & year<2000 & year >= 1990)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Americas" & year<2010 & year >= 2000)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Oceania" & year<1970 & year >= 1960)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Oceania" & year<1980 & year >= 1970)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Oceania" & year<1990 & year >= 1980)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Oceania" & year<2000 & year >= 1990)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
filter(gapminder_unfiltered,(continent=="Oceania" & year<2010 & year >= 2000)) %>%
ggplot() +
geom_freqpoly(aes(x=gdpPercap),binwidth=1)
fun_gdp <- function(continent, decade) {
a <- gapminder_unfiltered[which(gapminder_unfiltered$continent == continent & gapminder_unfiltered$decade == decade),]
stats2 <- c(min = min(a$gdpPercap), max =max( a$gdpPercap),
mean = mean(a$gdpPercap), median = median(a$gdpPercap),
IQR = quantile(a$gdpPercap, 0.75) - quantile(a$gdpPercap, 0.25))
return(stats2)
}
fun_gdp("Africa",1950)
## min max mean median IQR.75%
## 298.8462 5487.1042 1305.1360 982.0428 917.0443
gap <- data_by_decade(gapminder_unfiltered)
continentgdp <- c()
sta2<- c()
for(continent in levels(gap$continent)) {
for(decade in levels(gap$decade)) {
continentgdp <- rbind(continentgdp, c(continent, decade))
sta2 <- rbind(sta2, fun_gdp(continent, decade ))
}
}
## Warning in min(a$gdpPercap): no non-missing arguments to min; returning Inf
## Warning in max(a$gdpPercap): no non-missing arguments to max; returning -
## Inf
continentgdp
## [,1] [,2]
## [1,] "Africa" "1950"
## [2,] "Africa" "1960"
## [3,] "Africa" "1970"
## [4,] "Africa" "1980"
## [5,] "Africa" "1990"
## [6,] "Africa" "2000"
## [7,] "Americas" "1950"
## [8,] "Americas" "1960"
## [9,] "Americas" "1970"
## [10,] "Americas" "1980"
## [11,] "Americas" "1990"
## [12,] "Americas" "2000"
## [13,] "Asia" "1950"
## [14,] "Asia" "1960"
## [15,] "Asia" "1970"
## [16,] "Asia" "1980"
## [17,] "Asia" "1990"
## [18,] "Asia" "2000"
## [19,] "Europe" "1950"
## [20,] "Europe" "1960"
## [21,] "Europe" "1970"
## [22,] "Europe" "1980"
## [23,] "Europe" "1990"
## [24,] "Europe" "2000"
## [25,] "FSU" "1950"
## [26,] "FSU" "1960"
## [27,] "FSU" "1970"
## [28,] "FSU" "1980"
## [29,] "FSU" "1990"
## [30,] "FSU" "2000"
## [31,] "Oceania" "1950"
## [32,] "Oceania" "1960"
## [33,] "Oceania" "1970"
## [34,] "Oceania" "1980"
## [35,] "Oceania" "1990"
## [36,] "Oceania" "2000"
sta2
## min max mean median IQR.75%
## [1,] 298.8462 5487.104 1305.136 982.0428 917.0443
## [2,] 355.2032 18772.752 1803.591 1170.4843 1088.2434
## [3,] 464.0995 21951.212 2429.133 1351.0225 1682.5797
## [4,] 389.8762 17364.275 2362.302 1286.1727 1950.4980
## [5,] 312.1884 14722.842 2326.214 1186.1480 2025.2945
## [6,] 241.1659 13206.485 2844.420 1340.3506 2732.7562
## [7,] 1397.7171 15374.018 6515.719 4020.1861 8892.1625
## [8,] 1452.0577 20702.828 8019.659 5213.4317 10993.9097
## [9,] 1654.4569 25672.012 10109.298 6621.1344 13242.5668
## [10,] 1823.0160 31880.959 12040.224 7352.7580 17433.4639
## [11,] 1341.7269 39025.863 13843.705 7777.0534 20556.4788
## [12,] 1201.6372 42951.653 13809.123 8797.6407 12946.5861
## [13,] 331.0000 113523.133 4612.884 1487.5935 2469.8728
## [14,] 349.0000 95458.112 5390.930 2033.0565 5078.8165
## [15,] 357.0000 109347.867 10558.312 3692.2265 11673.9697
## [16,] 385.0000 62407.211 11416.603 6642.8814 16923.4615
## [17,] 347.0000 52938.653 12858.720 6454.2308 20557.5102
## [18,] 611.0000 82010.978 15087.881 5062.9683 23020.0301
## [19,] 973.5332 19059.005 7615.712 7205.5610 4799.3791
## [20,] 1709.6837 25862.819 11147.301 10837.7298 6601.6014
## [21,] 2860.1698 28732.366 15491.151 15610.7483 9021.4983
## [22,] 3630.8807 42467.638 18957.139 19297.6029 10892.9321
## [23,] 1830.2944 63924.163 21731.224 22979.6148 15067.3528
## [24,] 1933.6473 70014.000 26019.765 27987.1098 16308.4509
## [25,] Inf -Inf NaN NA NA
## [26,] 3029.7991 5470.687 4340.394 4444.3439 529.1452
## [27,] 3741.2261 11523.459 7092.228 6850.5960 1694.1270
## [28,] 6359.8299 13148.258 9431.813 9549.6922 1827.2758
## [29,] 1442.9378 12374.359 6378.065 6340.1208 3610.4404
## [30,] 1724.2955 16666.509 8660.943 9244.0066 5756.4405
## [31,] 10039.5956 13039.678 11322.296 11107.5436 1416.6949
## [32,] 1219.9966 20213.733 12059.751 13860.9581 2439.6813
## [33,] 864.9743 32001.307 13375.365 16633.4424 14103.5449
## [34,] 1467.3960 24837.871 14018.739 18740.6375 16794.8329
## [35,] 1880.9606 36383.170 16630.736 20768.7922 20147.2571
## [36,] 1530.4961 34435.367 15641.465 5565.1469 24343.7237
s2 <- data.frame(continentgdp,sta2)
names(s2)[1] <- "continent"
names(s2)[2] <- "decade"
s2
## continent decade min max mean median IQR.75.
## 1 Africa 1950 298.8462 5487.104 1305.136 982.0428 917.0443
## 2 Africa 1960 355.2032 18772.752 1803.591 1170.4843 1088.2434
## 3 Africa 1970 464.0995 21951.212 2429.133 1351.0225 1682.5797
## 4 Africa 1980 389.8762 17364.275 2362.302 1286.1727 1950.4980
## 5 Africa 1990 312.1884 14722.842 2326.214 1186.1480 2025.2945
## 6 Africa 2000 241.1659 13206.485 2844.420 1340.3506 2732.7562
## 7 Americas 1950 1397.7171 15374.018 6515.719 4020.1861 8892.1625
## 8 Americas 1960 1452.0577 20702.828 8019.659 5213.4317 10993.9097
## 9 Americas 1970 1654.4569 25672.012 10109.298 6621.1344 13242.5668
## 10 Americas 1980 1823.0160 31880.959 12040.224 7352.7580 17433.4639
## 11 Americas 1990 1341.7269 39025.863 13843.705 7777.0534 20556.4788
## 12 Americas 2000 1201.6372 42951.653 13809.123 8797.6407 12946.5861
## 13 Asia 1950 331.0000 113523.133 4612.884 1487.5935 2469.8728
## 14 Asia 1960 349.0000 95458.112 5390.930 2033.0565 5078.8165
## 15 Asia 1970 357.0000 109347.867 10558.312 3692.2265 11673.9697
## 16 Asia 1980 385.0000 62407.211 11416.603 6642.8814 16923.4615
## 17 Asia 1990 347.0000 52938.653 12858.720 6454.2308 20557.5102
## 18 Asia 2000 611.0000 82010.978 15087.881 5062.9683 23020.0301
## 19 Europe 1950 973.5332 19059.005 7615.712 7205.5610 4799.3791
## 20 Europe 1960 1709.6837 25862.819 11147.301 10837.7298 6601.6014
## 21 Europe 1970 2860.1698 28732.366 15491.151 15610.7483 9021.4983
## 22 Europe 1980 3630.8807 42467.638 18957.139 19297.6029 10892.9321
## 23 Europe 1990 1830.2944 63924.163 21731.224 22979.6148 15067.3528
## 24 Europe 2000 1933.6473 70014.000 26019.765 27987.1098 16308.4509
## 25 FSU 1950 Inf -Inf NaN NA NA
## 26 FSU 1960 3029.7991 5470.687 4340.394 4444.3439 529.1452
## 27 FSU 1970 3741.2261 11523.459 7092.228 6850.5960 1694.1270
## 28 FSU 1980 6359.8299 13148.258 9431.813 9549.6922 1827.2758
## 29 FSU 1990 1442.9378 12374.359 6378.065 6340.1208 3610.4404
## 30 FSU 2000 1724.2955 16666.509 8660.943 9244.0066 5756.4405
## 31 Oceania 1950 10039.5956 13039.678 11322.296 11107.5436 1416.6949
## 32 Oceania 1960 1219.9966 20213.733 12059.751 13860.9581 2439.6813
## 33 Oceania 1970 864.9743 32001.307 13375.365 16633.4424 14103.5449
## 34 Oceania 1980 1467.3960 24837.871 14018.739 18740.6375 16794.8329
## 35 Oceania 1990 1880.9606 36383.170 16630.736 20768.7922 20147.2571
## 36 Oceania 2000 1530.4961 34435.367 15641.465 5565.1469 24343.7237
The highest GDP per capita lies in developed countries, and their changes a smaller than Asia. But Asia has a great increase by decades.
I used several functions to form two tables, one is for the changes of life expectancy by continents by decade, and another is the changes of GDP per capita by continent by decades. And those two tables illustrated 5 statsitics for each decade and continent. We could see the increaseing tendency of changing of life expectancy from those continents, especially Asia. And the great increasing in GDP in Asia too.
I tried to analysis the 5 decades the life expectancy and the gdp per capita, first time I just used subset to five indiviual continent and use the aggregate function tried to calculate the answer, but the only things that i can find is the averge answer durning the whole time and I stuck in every decades, so I used another way tried to find the relation durning the plots. for each question I created 5*5 plots. 5 contients and 5 decedes, and from the graphs that I got, it is really easy to conclude that the life expectancy and the cgp per capita increase when the years increase.